1. Field of the Invention
The present invention relates to web page content optimization, and more particularly, to optimizing page content by moderating changes contributed by users based on confidence levels.
2. Description of the Related Art
Internet searching has become ubiquitous with web navigation. During web navigation, a query for a topic page is received at a server. An algorithm, such as a glue algorithm, on the search engine is used to identify a topic category associated with the query, search a repository of information available to the search engine to identify a plurality of contents that match the query. The contents are provided in the form of modules. A topic page is generated using the identified content modules. The modules within the topic page may include a variety of contents in varied formats that are most relevant to the query. The topic page is associated with the most popular topic category for the query. The generated topic page is returned in response to the query.
The returned topic page may be customized by a user to suit his needs. As part of customization, one or more modules may be added, deleted or relocated. Additionally, one or more modules from the algorithm generated topic page may be replaced by one or more user defined modules. The customized topic page may be stored in the repository under the user so that the customized topic page may be rendered for subsequent query by the user.
When a subsequent query is received at the server, the algorithm within the server identifies the topic category, searches the repository of information to determine a topic page matching the query and returns the topic page in response to the query. There is, however, a constant quest to determine which topic page is a best match for the query, whether the user generated topic page is better than the algorithmically generated topic page and if the returned topic page is optimal or not.
One way of determining the optimal topic page is through a mechanism called bucket-testing. The bucket testing mechanism uses user interaction at each of the topic pages to determine the popularity of the corresponding topic pages. User interactions, such as click-throughs, resident time at the topic page including resident time at each of the modules, etc., are gathered and the popularity of the topic page is determined. Based on the determination, the most popular topic page is considered as the most optimal for the query.
One of the issues with the bucket testing mechanism is that it requires a fairly large sample of user data/interactions in order to determine the popularity of the topic page. As a result, when a topic page is very infrequently accessed or has insufficient amount of user interactions, it is difficult to determine if the topic page is optimal or not. If a change needs to be made to such topic pages, it will take a long time to obtain a large enough data sample in order to determine if the change was optimal or not. As a result, it is difficult to determine which topic page is a better match for the query in a short period of time. As a result, for a topic page with insufficient user interaction data, bucket testing becomes tedious, time-consuming and often impractical to use.
It is in this context that the embodiments of the invention arise.